压缩网格提升联邦学习中KAN的通信效率
CG-FKAN: Compressed-Grid Federated Kolmogorov-Arnold Networks for Communication Constrained Environment
- 用稀疏化传输关键系数压缩扩展网格
- 通信受限下比固定网格KAN误差低13.6%
- 理论给出近似误差上界，适合资源受限场景
联邦学习（FL）广泛应用于隐私敏感场景，但现有方法可解释性差。科尔莫戈罗夫-阿诺尔德网络（KAN）通过可学习样条函数改善了这一问题。然而，已有将KAN用于联邦学习的研究忽略了网格扩展带来的通信开销，而网格扩展对建模复杂函数至关重要。本文提出CG-FKAN，通过稀疏化并仅传输必要系数，在通信预算下压缩扩展网格。实验表明，CG-FKAN在通信受限环境下相比固定网格KAN的均方根误差降低最多达13.6%。此外，我们推导了其近似误差的理论上限。
原文摘要 · Abstract (English)
Federated learning (FL), widely used in privacy-critical applications, suffers from limited interpretability, whereas Kolmogorov-Arnold Networks (KAN) address this limitation via learnable spline functions. However, existing FL studies applying KAN overlook the communication overhead introduced by grid extension, which is essential for modeling complex functions. In this letter, we propose CG-FKAN, which compresses extended grids by sparsifying and transmitting only essential coefficients under a communication budget. Experiments show that CG-FKAN achieves up to 13.6% lower RMSE than fixed-grid KAN in communication-constrained settings. In addition, we derive a theoretical upper bound on its approximation error.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。